Struct SubstationSiting

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pub struct SubstationSiting {
    pub load_points: Vec<(f64, f64, f64)>,
    pub n_substations: usize,
    pub voltage_kv: f64,
    pub cable_cost_million_eur_per_km: f64,
}

Expand description

Geographic substation siting via load-weighted k-means clustering.

Each cluster centroid represents an optimal substation location that minimises the total weighted distance (≈ cable length) to served load points.

Fields§

§load_points: Vec<(f64, f64, f64)>

Load points: (x_km, y_km, load_mw).

§n_substations: usize

Desired number of substations.

§voltage_kv: f64

Nominal voltage of the distribution feeder [kV].

§cable_cost_million_eur_per_km: f64

Cable cost per km [million EUR/km].

Implementations§

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impl SubstationSiting

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pub fn new( load_points: Vec<(f64, f64, f64)>, n_substations: usize, voltage_kv: f64, cable_cost_million_eur_per_km: f64, ) -> Self

Construct a new siting problem.

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pub fn optimize_kmeans( &self, max_iter: usize, ) -> Result<SitingResult, OxiGridError>

Optimise substation locations using load-weighted k-means.

Initialisation: spread substations by percentile of x-coordinate, choosing the load-weighted centroid within each segment as the initial centre.

Iteration: assign each load point to its nearest substation (Euclidean), recompute load-weighted centroids. Converge when centroid shift < 0.01 km.

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pub fn euclidean_distance(p1: (f64, f64), p2: (f64, f64)) -> f64

Euclidean distance between two 2D points.

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pub fn load_weighted_centroid(points: &[(f64, f64, f64)]) -> (f64, f64)

Load-weighted centroid of a set of (x, y, load_mw) points.

Returns the unweighted centroid if total load is zero.

Auto Trait Implementations§

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impl UnwindSafe for SubstationSiting

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